Naive Scene Graphs: How Visual is Modern Visual Relationship Detection?
Bibliographic record
Abstract
Modern approaches to scene graph generation still struggle with their performance, with even state of the art approaches hovering under a 15% mean recall on certain evaluation modes. This poor performance is partially a result of networks heavily relying and fixating on non-visual data, such as class statistics, instead of the pixel-level signals present in the images. We demonstrate this by examining the 'visual-ness' of visual relationship detection approaches. We first describe and implement a new Naive Bayes-based statistical baseline for scene graph generation. Most notably, this basic classifier does not utilize the image pixels, but relies on the properties of the bounding boxes (class labels, topological configuration, … etc.) to predict the relationship labels. We demonstrate that our classical machine learning approach, one as simple as a categorical Naive Bayes classifier, can perform relationship detection in a manner that achieves relatively competitive performance to that of modern scene graph generators. This is an alarming finding regarding scene graph generation that implies that visual data in images may not be utilized in modern visual relationship detection past the point of object detection. We finally discuss how more visual modern approaches to scene graph generation appear to remedy some of these shortcomings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".